Agentic AI: Architectures, Types, Capabilities, Mathematical Equations and Governance in the Era of Autonomous Intelligence

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 03 | 02 | Page :
By

Dr. Nitin S. Shrirao,

Mr. Dnyaneshwar S. Jadhav,

Mrs. Sarita B Patil,

  1. Assistant Professor, Siddhant Institute of Computer Applications, Pune, India
  2. Assistant Professor, Siddhant Institute of Computer Applications, Pune, India
  3. Assistant Professor, Siddhant Institute of Computer Applications, Pune, India

Abstract

Agentic Artificial Intelligence (Agentic AI) represents a major advancement in the evolution of intelligent systems by enabling autonomous planning, decision-making, and action execution. Unlike traditional AI models, which are primarily reactive and designed to respond to predefined inputs, Agentic AI systems possess capabilities such as memory, reasoning, goal-oriented planning, tool integration, and dynamic adaptation to changing environments. These characteristics allow them to perform complex, multi-step tasks with minimal human intervention, making them suitable for applications across healthcare, finance, cybersecurity, robotics, education, and enterprise automation. This paper explores the conceptual foundations, architectural principles, and practical applications of Agentic AI while examining the key differences between conventional AI and autonomous agent-based systems. It presents a taxonomy that distinguishes reactive and agentic models, discusses single-agent and multi-agent orchestration frameworks, and analyzes the role of memory, planning, perception, and external tool usage in intelligent decision-making. The study also highlights the ethical, security, governance, and accountability challenges associated with deploying autonomous AI, including issues related to transparency, privacy, bias, reliability, and human oversight. As AI systems become increasingly capable of operating independently within complex environments, establishing robust governance and regulatory frameworks is essential. This paper proposes a comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance. By integrating technical innovation with effective governance strategies, the proposed approach aims to maximize the benefits of Agentic AI while minimizing potential risks. The findings contribute to the growing body of research on trustworthy autonomous systems and provide practical guidance for developing secure, reliable, and human-centered Agentic AI solutions.

Keywords: Artificial Intelligence(AI), large language models (LLMs), Reinforcement learning (RL), Markov Decision Process (MDP), Multi-Agent System (MAS), Hierarchical Multi-Agent System(HMAS), Robot Operating System (ROS), Non-Player Characters (NPCs), Human-in-the-Loop (HITL)

How to cite this article: Dr. Nitin S. Shrirao, Mr. Dnyaneshwar S. Jadhav, Mrs. Sarita B Patil. Agentic AI: Architectures, Types, Capabilities, Mathematical Equations and Governance in the Era of Autonomous Intelligence. Recent Trends in Mathematics. 2026; 03(02):-.
How to cite this URL: Dr. Nitin S. Shrirao, Mr. Dnyaneshwar S. Jadhav, Mrs. Sarita B Patil. Agentic AI: Architectures, Types, Capabilities, Mathematical Equations and Governance in the Era of Autonomous Intelligence. Recent Trends in Mathematics. 2026; 03(02):-. Available from: https://journals.stmjournals.com/rtm/article=2026/view=250844

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Ahead of Print Subscription Review Article
Volume 03
02
Received 12/02/2026
Accepted 23/07/2026
Published 30/07/2026
Publication Time 168 Days

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